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About Bryan Catanzaro

Bryan Catanzaro is a senior research scientist at Baidu's Silicon Valley AI Lab, where he leads the systems team. His research is focused on efficient tools and methodologies for training large deep neural networks. Before joining Baidu, Bryan worked at NVIDIA Research, where he contributed to the cuDNN library. Bryan received his PhD from the University of California at Berkeley, where he wrote the first Support Vector Machine training library to run on GPUs, and created Copperhead, a Python-based DSL for parallel programming.

Speech recognition is an established technology, but it tends to fail when we need it the most, such as in noisy or crowded environments, or when the speaker is far away from the microphone. At Baidu we are working to enable truly ubiquitous, natural speech interfaces. In order to achieve this, we must improve the accuracy of speech recognition, especially in these challenging environments. We set out to make progress towards this goal by applying Deep Learning in a new way to speech recognition.

Figure 1: The structure of our deep neural network, showing the layers (top to bottom) and how we parallelize training across GPUs (left to right). The fourth layer is a bidirectional recurrent layer. Blue and red arrows indicate the forward and backward direction and the communication required between GPUs in these layers.

Deep Learning has transformed many important tasks; it has been successful because it scales well: it can absorb large amounts of data to create highly accurate models. Indeed, most industrial speech recognition systems rely on Deep Neural Networks as a component, usually combined with other algorithms. Many researchers have long believed that Deep Neural Networks (DNNs) could provide even better accuracy for speech recognition if they were used for the entire system, rather than just as the acoustic modeling component. However, it has proven difficult to find an end-to-end speech recognition system based on Deep Learning that improves on the state of the art.

Model and Data Co-design

One of the reasons this has been difficult is that training these networks on large datasets is computationally very intensive. The process of training DNNs is iterative: we instantiate ideas about models in computer code that trains a model, then we train the model on a training set and test it, which gives us new ideas about how to improve the model or training set. The latency of this loop is the rate limiting step that gates progress. Our models are relatively large, containing billions of connections, and we train them on thousands of hours of data, which means that training our models takes a lot of computation. Continue reading →

Programming environments like C and Fortran allow complete and unrestricted access to computing hardware, but often require programmers to understand the low-level details of the hardware they target. Although these efficiency-oriented systems are essential to every computing platform, many programmers prefer to use higher level programming environments like Python or Ruby, focused on productivity rather than absolute performance. Productivity-focused programmers solving large or intensive problems do need high performance, and many seek to exploit parallel computing, but without the costs of understanding low-level hardware details or programming directly to a particular machine.

Copperhead is a project that aims to enable productivity-focused programmers to take advantage of parallel computing, without explicitly coding to any particular machine. Copperhead programs use familiar Python primitives such as map and reduce, and they execute in parallel on both CUDA-enabled GPUs as well as multicore
CPUs.

Parallel Hello World: axpy

Let’s start with an example: below find Copperhead code for axpy, the “hello world” of parallel programs. (axpy is the type-generic form of saxpy. See Six Ways to SAXPY for more.)